Graph-theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Alzheimer's disease (AD). Recent studies suggest that representing brain networks in a suitable geometric space can better capture their connectivity structure. This study introduces a novel approach to characterize brain connectivity changes using low-dimensional, informative representations of networks in a latent geometric space. Specifically, the networks are embedded in a polar representation of the hyperbolic plane, the hyperbolic disk. Here, we used a geometric score, entirely based on the computation of distances between nodes in the latent space, to measure the effect of a perturbation on the nodes. Precisely, the score is a local measure of distortion in the geometric neighborhood of a node following a perturbation. The method is applied to a brain network dataset of patients with AD and healthy participants, derived from diffusion-weighted (DWI) and functional magnetic resonance (fMRI) imaging scans. We show that, compared with standard graph measures, our method more accurately identifies the brain regions most affected by neurodegeneration. Notably, the abnormalities detected in memory-related and frontal areas are robust across multiple brain parcellation scales. Finally, our findings suggest that the geometric perturbation score could serve as a potential biomarker for characterizing the progression of the disease.

The physics of planetary climate features a variety of complex systems that are challenging to model as they feature turbulent flows. A key example is the heat flux from the upper ocean to the underside of sea ice which provides a key contribution to the evolution of the Arctic sea ice cover. Here, we develop a model of the turbulent ice-ocean heat flux using coupled ordinary stochastic differential equations to model fluctuations in the vertical velocity and temperature in the Arctic mixed layer. All the parameters in the model are determined from observational data. A detailed comparison between the model results and measurements made during the Surface Heat Budget of the Arctic Ocean (SHEBA) project reveals that the model is able to capture the probability density functions (PDFs) of velocity, temperature, and heat flux fluctuations. Furthermore, we show that the temperature in the upper layer of the Arctic Ocean can be treated as a passive scalar during the whole year of SHEBA measurements. The stochastic model developed here provides a computationally inexpensive way to compute an observationally consistent PDF of this heat flux and has implications for its parametrization in regional and global climate models.

We report on record brightness from Ag x-ray emission obtained using a novel laser-produced plasma source. The reported K-shell conversion efficiency of nearly 1% with a radiant energy of $\approx 0.6$ kJ/sr from Ag ions is the highest presently recorded and is about twofold greater than more conventional metal-lined cavity targets. He-like Ag ions are the dominant radiators at $\approx 22.7$ keV, which contrast the x-ray sources from other pulse power facilities that produce K-shell x-rays from Auger processes in near-neutral ions driven by the nonthermal hot electrons produced in the Z-pinch implosion. The reported x-ray emission was produced from a 4-mm-diameter, 4-mm-long underdense silver nanowire target at $10\mathrm{mg}/{\mathrm{cm}}^3$. The National Ignition Facility laser beams deposited $\approx 1000$ kJ of $3\omega$ light into the target with a $\approx 400$ TW, 2.5 ns square pulse. Finally, we show via comparisons of data with radiation-hydrodynamics simulations that flux-limited classical thermal energy transport models are inadequate in correctly modeling the behavior of these non-local thermodynamic equilibrium plasmas.

A persistent public health challenge is identifying immunization schemes that are effective against highly mutable pathogens such as HIV and influenza viruses. To address this, we analyze a simplified model of affinity maturation, the Darwinian evolutionary process B cells undergo during immunization. The vaccination protocol determines the selection forces that steer affinity maturation to generate antibodies. We focus on identifying the optimal selection forces exerted by a generic time-dependent vaccination protocol to maximize the production of broadly neutralizing antibodies (bnAbs) that can protect against a broad spectrum of pathogen strains. The model utilizes a path integral representation and operator approximations within a mean-field limit and provides guiding principles for optimizing time-dependent vaccine-induced selection forces to enhance bnAb generation. We compare our analytical mean-field results with the outcomes of stochastic simulations, and we discuss their similarities and differences.

Circulating tumor cell clusters play an important role in the metastatic cascade. These clusters can acquire a migratory and more invasive phenotype, and coordinate their motion to migrate as a collective. Before such clusters can form by collectively detaching from a primary tumor, however, the cluster must first aggregate in the tumor interior. The mechanism of this cluster formation process is still poorly understood. One of the possible ways for cells to cluster is by aligning their direction of motion with their neighboring cells. This work aims to investigate the role of this cell-cell alignment interaction on the formation of motile cell clusters inside the bulk of a tumor using computer simulations. We employ a cellular Potts model in which we model a two-dimensional heterogeneous confluent layer containing both motile and nonmotile cells. Our results indicate that the degree of clustering is governed by two distinct processes: the formation of clusters due to the presence of cell-cell alignment interactions among motile cells, and the suppression of clustering due to the presence of the dynamic cellular environment (comprising the nonmotile cells). We find that the largest motile clusters are formed for intermediate alignment strengths, contrary to what is observed for motile cells in free space (that is, unimpeded by a dense cellular environment), in which case stronger cell-cell alignment always leads to larger clustering. Our findings suggest that the presence of a densely packed cellular environment and strong cell-cell alignment inhibits the formation of large migratory clusters within the primary tumor, providing physical insight into potential factors at play during the early stages of metastasis.

In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to sift through them. We present an algorithm to automatically identify and track clusters of radar echoes through time, using dbscan, a celebrated density-based clustering method for noisy point clouds. We demonstrate our algorithm's efficiency by tracking turbulent structures in the E-region ionosphere, the so-called radar aurora. Through conjugate auroral imagery, as well as in situ satellite observations, we demonstrate that the observed turbulent structures generally track the motion of auroras. What is more, the radar aurora bulk motions exhibit key qualities of auroral electric field enhancements that have previously been observed with various instruments. We present preliminary statistical results using our method, and briefly discuss the method's limitations and potential future adaptations.

Recent empirical studies have found different thermodynamic phases for collective motion in animals. However, such a thermodynamic description of human movement remains unclear. Existing studies of traffic and pedestrian flows have primarily focused on relatively high-speed mobility data, revealing only a fluidlike phase. This focus is partly because the parameter space of low-speed movement, which is governed predominantly by pairwise social interaction, remains largely uncharted. Here, we used ultrawideband radio frequency identification (UWB-RFID) technology to collect high-resolution spatiotemporal data on movements in four different classroom and playground settings. We observed two unique social phases in children's movements: a gaslike phase of free movement and a liquid-vapor coexistence phase characterized by the formation of small social groups. We also developed a simple statistical physics model that can reproduce different empirically observed phases. The proposed UWB-RFID technology can also be used to study the dynamics of active matter systems, including animal behavior, coordinating robotic swarms, and monitoring human interactions within complex systems, potentially benefiting future research in social physics.

Media with correlated disorder display unexpected transport properties, but it is still a challenge to design structures with desired spectral features at scale. In this work, we introduce an optimal formulation of this inverse problem by means of the nonuniform fast Fourier transform, thus arriving at an algorithm capable of generating systems with arbitrary spectral properties, with a computational cost that scales $\mathcal{O}(NlogN)$ with system size. The method is extended to accommodate arbitrary real-space interactions, such as short-range repulsion, to simultaneously control short- and long-range correlations. We thus generate the largest-ever stealthy hyperuniform configurations in $2d$ ($N={10}^9$) and $3d$ ($N>{10}^7$) and demonstrate the flexibility of the approach by generating structures with designed spectral features at scale. By an Ewald sphere construction we link the spectral and optical properties at the single-scattering level and show that stealthy hyperuniform structures generically display transmission gaps, providing a concrete example of fine-tuning of a physical property. We also show that large $3d$ power-law hyperuniformity in particle packings leads to single-scattering properties nearly identical to those of simple hard spheres. Finally, we demonstrate generalizations of the approach to impose features in either continuous or discrete real space, using constraints in either continuous or discrete reciprocal space. In particular, enforcing large spectral power at peaks with the right symmetry leads to the nondeterministic generation of quasicrystalline structures in $2d$ and $3d$. This technique should become an essential tool to embed, and understand the role of, long-range correlations in disordered metamaterials.

Many animals have patterned fur, feathers, or scales, such as the stripes of a zebra. Turing models, or reaction-diffusion systems, are a class of mathematical models of interacting species that have been successfully used to generate animal-like patterns for many species. When diffusion of the inhibitor is high enough relative to the activator, a diffusion-driven instability can spontaneously form patterns. However, it is not just the type of pattern but also the orientation that matters, and it remains unclear how patterns are oriented in practice. Here, we propose a mechanism by which the curvature of the surface influences the rate of diffusion, and can recapture the correct orientation of stripes on models of a zebra and of a cat in numerical simulations. Previous work has shown how anisotropic diffusion can give stripe forming reaction-diffusion systems a bias in orientation. From the observation that zebra stripes run around the direction of highest curvature, that is around the torso and legs, we apply this result by modifying the anisotropic diffusion rates based on the local curvature. These results show how local geometry can influence the reaction dynamics to give robust, global-scale patterns. Overall, this model proposes a coupling between the system geometry and reaction-diffusion dynamics that can give global control over the patterning by using only local curvature information. Such a model can give shape and positioning information in animal development without the need for spatially dependent morphogen gradients.

We investigate the collective behavior of a system of social agents subject to the competition between two mass media influences considered as external fields. We study under what conditions either of two mass media with different intensities can impose its message to the majority. In addition to a collective state dominated by the stronger mass media and a disordered phase, we characterize two nontrivial effects as the parameters of the system are varied: (i) the appearance of a majority sharing the state of the weaker mass media, and (ii) the emergence of an alternative ordering in a state different from those of either media. We explore the dependence of both phenomena on the topology of the network of interactions. We show that the presence of long-range interactions rather than random connections is essential for the occurrence of both effects. The model can be extended to include multiple mass media and we illustrate it by considering three mass media fields acting on the system. Nontrivial collective behaviors persist for some ranges of parameters: the weakest mass media can convince the majority, and the system can spontaneously order against all applied fields.

Time delays play a significant role in dynamical systems, as they affect their transient behavior and the dimensionality of their attractors. The number, values, and spacing of these time delays influences the eigenvalues of a nonlinear delay-differential system at its fixed point. Here we explore a multidelay system as the core computational element of a reservoir computer making predictions on its input in the usual regime close to fixed point instability. Variations in the number and separation of time delays are first examined to determine the effect of such parameters of the delay distribution on the effectiveness of time-delay reservoirs for nonlinear time series prediction. We demonstrate computationally that an optoelectronic device with multiple different delays can improve the mapping of scalar input into higher-dimensional dynamics, and thus its memory and prediction capabilities for input time series generated by low- and high-dimensional dynamical systems. In particular, this enhances the suitability of such reservoir computers for predicting input data with temporal correlations. Additionally, we highlight the pronounced harmful resonance condition for reservoir computing when using an electro-optic oscillator model with multiple delays. We illustrate that the resonance point may shift depending on the task at hand, such as cross prediction or multistep ahead prediction, in both single delay and multiple delay cases.

We show in experiments that a long, underdense, relativistic proton bunch propagating in plasma undergoes the oblique instability, which we observe as filamentation. We determine a threshold value for the ratio between the bunch transverse size and plasma skin depth for the instability to occur. At the threshold, the outcome of the experiment alternates between filamentation and self-modulation instability (evidenced by longitudinal modulation into microbunches). Time-resolved images of the bunch density distribution reveal that filamentation grows to an observable level late along the bunch, confirming the spatiotemporal nature of the instability. We provide a rough estimate of the amplitude of the magnetic field generated in the plasma by the instability and show that the associated magnetic energy increases with plasma density.

We demonstrate that nanofluidic diodes in multipore membranes show a memristive behavior that can be controlled not only by the amplitude and frequency of the external signal but also by series and parallel arrangements of the membranes. Each memristor consists of a polymeric membrane with conical nanopores that allow current rectification due to the electrical interaction between the ionic solution and the pore surface charges. This surface charge-regulated ionic transport shows a rich nonlinear physics, including memory and inductive effects, which are characterized here by the current-voltage curves and electrical impedance spectroscopy. Also, neuromorphiclike potentiation of the membrane conductance following voltage pulses (spikes) is observed. The multipore membrane with nanofluidic diodes shows physical concepts that should have application for information processing and signal conversion in iontronics hybrid devices.

Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process that regulates the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis, proposing a predictor that could be particularly interesting to monitor the ecological resilience of the world's “green lungs.”

An indirect-drive inertial fusion experiment on the National Ignition Facility was driven using 2.05 MJ of laser light at a wavelength of 351 nm and produced $3.1\pm 0.16$ MJ of total fusion yield, producing a target gain $G=1.5\pm 0.1$ exceeding unity for the first time in a laboratory experiment [Phys. Rev. E 109, 025204 (2024)]. Herein we describe the experimental evidence for the increased drive on the capsule using additional laser energy and control over known degradation mechanisms, which are critical to achieving high performance. Improved fuel compression relative to previous megajoule-yield experiments is observed. Novel signatures of the ignition and burn propagation to high yield can now be studied in the laboratory for the first time.

In this work we present the design of the first controlled fusion laboratory experiment to reach target gain $G>1$ N221204 (5 December 2022) [Phys. Rev. Lett. 132, 065102 (2024)], performed at the National Ignition Facility, where the fusion energy produced (3.15 MJ) exceeded the amount of laser energy required to drive the target (2.05 MJ). Following the demonstration of ignition according to the Lawson criterion N210808, experiments were impacted by nonideal experimental fielding conditions, such as increased (known) target defects that seeded hydrodynamic instabilities or unintentional low-mode asymmetries from nonuniformities in the target or laser delivery, which led to reduced fusion yields less than 1 MJ. This Letter details design changes, including using an extended higher-energy laser pulse to drive a thicker high-density carbon (also known as diamond) capsule, that led to increased fusion energy output compared to N210808 as well as improved robustness for achieving high fusion energies (greater than 1 MJ) in the presence of significant low-mode asymmetries. For this design, the burnup fraction of the deuterium and tritium (DT) fuel was increased (approximately $4\%$ fuel burnup and a target gain of approximately 1.5 compared to approximately $2\%$ fuel burnup and target gain approximately 0.7 for N210808) as a result of increased total (DT plus capsule) areal density at maximum compression compared to N210808. Radiation-hydrodynamic simulations of this design predicted achieving target gain greater than 1 and also the magnitude of increase in fusion energy produced compared to N210808. The plasma conditions and hotspot power balance (fusion power produced vs input power and power losses) using these simulations are presented. Since the drafting of this manuscript, the results of this paper have been replicated and exceeded (N230729) in this design, together with a higher-quality diamond capsule, setting a new record of approximately $3.88\mathrm{MJ}$ of fusion energy and fusion energy target gain of approximately 1.9.

We introduce a numerical method to extract the parameters of run-and-tumble dynamics from experimental measurements of the intermediate scattering function. We show that proceeding in Laplace space is unpractical and employ instead renewal processes to work directly in real time. We first validate our approach against data produced using agent-based simulations. This allows us to identify the length and time scales required for an accurate measurement of the motility parameters, including tumbling frequency and swim speed. We compare different models for the run-and-tumble dynamics by accounting for speed variability at the single-cell and population level, respectively. Finally, we apply our approach to experimental data on wild-type Escherichia coli obtained using differential dynamic microscopy.

The unique ability of fire ants to form a raft to survive flooding rain has enchanted biologists as well as researchers in other disciplines. It was established during the last decade that a three-dimensional aggregation of fire ants exhibits viscoelasticity with respect to external compression and shearing among numerous unusual mechanical properties. Continuing these works, we will study the ant raft in its natural form, i.e., composing no more than two layers. This allowed us to focus on the cracks that are unique to membranes and see how their patterns are influenced by the fact that these ants are mobile and can self-repair the damage to keep their raft from disintegration. In the beginning, we show that vertical and horizontal shaking can also prompt fire ants to aggregate. The canonical view that the stability of ant raft relies on the Cheerios effect and a combination of other parameters is tested. The force-displacement experiment is performed to show that two distinct mechanical responses and fracture patterns, characteristic of ductile and brittle materials, can be elicited, depending on the magnitude of the pull speed. During the process, we counted the number of ants that actively participated in the stress-strain relation and used this information to roughly sketch out the force chain. The latter information reveals that the pull force expedites the alignment of fire ants, in analogy to the effect of an electric field on liquid crystal polymers. To highlight the self-healing nature, we employ the creep experiment to study how the length and Young's modulus of the raft change or relax with time. One major finding is that the raft can exhibit zero Poisson's ratio without resorting to specific geometry structures. This is enabled by the active recruitment of ants from the top layer to the bottom layer to keep the raft from disintegrating.

Craters formed by the impact of agglomerated materials are commonly observed in nature, such as asteroids colliding with planets and moons. In this paper, we investigate how the projectile spin and cohesion lead to different crater shapes. For that, we carried out discrete element method computations of spinning granular projectiles impacting onto cohesionless grains for different bonding stresses, initial spins, and initial heights. We found that, as the bonding stresses decrease and the initial spin increases, the projectile's grains spread farther from the collision point, and in consequence, the crater shape becomes flatter, with peaks around the rim and in the center of the crater. Our results shed light on the dispersion of the projectile's material and the different shapes of craters found on Earth and other planetary environments.

We generate nonlattice packings of spheres in up to 22 dimensions using the geometrical constraint satisfaction algorithm RRR. Our aggregated data suggest that it is easy to double the density of Ball's lower bound and, more tentatively, that the exponential decay rate of the density can be improved relative to Minkowski's longstanding 1/2.